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[MINOR][SPARKR][ML] Joint coefficients with intercept for SparkR linear SVM summary. #18035

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[MINOR][SPARKR][ML] Joint coefficients with intercept for SparkR linear SVM summary. #18035

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1ed3ba0
Code reorg and cleanup for SparkR linear SVM.
yanboliang May 19, 2017
39317c1
Update test case.
yanboliang May 19, 2017
3c14d15
Update docs.
yanboliang May 22, 2017
5d9afe0
Fix typo.
yanboliang May 23, 2017
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40 changes: 16 additions & 24 deletions R/pkg/R/mllib_classification.R
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Expand Up @@ -46,15 +46,16 @@ setClass("MultilayerPerceptronClassificationModel", representation(jobj = "jobj"
#' @note NaiveBayesModel since 2.0.0
setClass("NaiveBayesModel", representation(jobj = "jobj"))

#' linear SVM Model
#' Linear SVM Model
#'
#' Fits an linear SVM model against a SparkDataFrame. It is a binary classifier, similar to svm in glmnet package
#' Fits a linear SVM model against a SparkDataFrame, similar to svm in e1071 package.
#' Currently only supports binary classification model with linear kernal.
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Do you mean kernel instead of kernal?

#' Users can print, make predictions on the produced model and save the model to the input path.
#'
#' @param data SparkDataFrame for training.
#' @param formula A symbolic description of the model to be fitted. Currently only a few formula
#' operators are supported, including '~', '.', ':', '+', and '-'.
#' @param regParam The regularization parameter.
#' @param regParam The regularization parameter. Only supports L2 regularization currently.
#' @param maxIter Maximum iteration number.
#' @param tol Convergence tolerance of iterations.
#' @param standardization Whether to standardize the training features before fitting the model. The coefficients
Expand Down Expand Up @@ -111,10 +112,10 @@ setMethod("spark.svmLinear", signature(data = "SparkDataFrame", formula = "formu
new("LinearSVCModel", jobj = jobj)
})

# Predicted values based on an LinearSVCModel model
# Predicted values based on a linear SVM model.
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I think these are intentional - we have # Predicted values based on an LogisticRegressionModel model
they are prefix by # and not in generated doc - only for developers

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there are a couple of these starting with #


#' @param newData a SparkDataFrame for testing.
#' @return \code{predict} returns the predicted values based on an LinearSVCModel.
#' @return \code{predict} returns the predicted values based on a linear SVM model.
#' @rdname spark.svmLinear
#' @aliases predict,LinearSVCModel,SparkDataFrame-method
#' @export
Expand All @@ -124,39 +125,30 @@ setMethod("predict", signature(object = "LinearSVCModel"),
predict_internal(object, newData)
})

# Get the summary of an LinearSVCModel
# Get the summary of a linear SVM model.

#' @param object an LinearSVCModel fitted by \code{spark.svmLinear}.
#' @param object a linear SVM model fitted by \code{spark.svmLinear}.
#' @return \code{summary} returns summary information of the fitted model, which is a list.
#' The list includes \code{coefficients} (coefficients of the fitted model),
#' \code{intercept} (intercept of the fitted model), \code{numClasses} (number of classes),
#' \code{numFeatures} (number of features).
#' \code{numClasses} (number of classes), \code{numFeatures} (number of features).
#' @rdname spark.svmLinear
#' @aliases summary,LinearSVCModel-method
#' @export
#' @note summary(LinearSVCModel) since 2.2.0
setMethod("summary", signature(object = "LinearSVCModel"),
function(object) {
jobj <- object@jobj
features <- callJMethod(jobj, "features")
labels <- callJMethod(jobj, "labels")
coefficients <- callJMethod(jobj, "coefficients")
nCol <- length(coefficients) / length(features)
coefficients <- matrix(unlist(coefficients), ncol = nCol)
intercept <- callJMethod(jobj, "intercept")
features <- callJMethod(jobj, "rFeatures")
coefficients <- callJMethod(jobj, "rCoefficients")
coefficients <- as.matrix(unlist(coefficients))
colnames(coefficients) <- c("Estimate")
rownames(coefficients) <- unlist(features)
numClasses <- callJMethod(jobj, "numClasses")
numFeatures <- callJMethod(jobj, "numFeatures")
if (nCol == 1) {
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@yanboliang yanboliang May 19, 2017 •
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ML LinearSVC only supports binary classification, and will not support multiple classification in the near future, so we can simplify here.

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why not label, intercept? i think they are common in R to include what goes into the model (although in many cases it just include the formula in the model summary)

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@yanboliang yanboliang May 22, 2017 •
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@felixcheung The change here is to make coefficients matrix has only one column named Estimate. I speculate the original code referred to spark.logit which supports multiple classification, so it should have multiple columns and each columns' name should be corresponding label. For binary classification, the coefficients are not bind to any labels, so we use Estimate as the column name like what R does. LinearSVC will not support multiple classification in the future, so I simplified it at here.
The followings are summary outputs for binomial and multinomial logistic regression in SparkR:
Binomial logistic regression model:
image
Multinomial logistic regression model:
image

colnames(coefficients) <- c("Estimate")
} else {
colnames(coefficients) <- unlist(labels)
}
rownames(coefficients) <- unlist(features)
list(coefficients = coefficients, intercept = intercept,
numClasses = numClasses, numFeatures = numFeatures)
list(coefficients = coefficients, numClasses = numClasses, numFeatures = numFeatures)
})

# Save fitted LinearSVCModel to the input path
# Save fitted linear SVM model to the input path.

#' @param path The directory where the model is saved.
#' @param overwrite Overwrites or not if the output path already exists. Default is FALSE
Expand Down
3 changes: 1 addition & 2 deletions R/pkg/inst/tests/testthat/test_mllib_classification.R
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Expand Up @@ -38,9 +38,8 @@ test_that("spark.svmLinear", {
expect_true(class(summary$coefficients[, 1]) == "numeric")

coefs <- summary$coefficients[, "Estimate"]
expected_coefs <- c(-0.1563083, -0.460648, 0.2276626, 1.055085)
expected_coefs <- c(-0.06004978, -0.1563083, -0.460648, 0.2276626, 1.055085)
expect_true(all(abs(coefs - expected_coefs) < 0.1))
expect_equal(summary$intercept, -0.06004978, tolerance = 1e-2)

# Test prediction with string label
prediction <- predict(model, training)
Expand Down
12 changes: 10 additions & 2 deletions mllib/src/main/scala/org/apache/spark/ml/r/LinearSVCWrapper.scala
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Expand Up @@ -38,9 +38,17 @@ private[r] class LinearSVCWrapper private (
private val svcModel: LinearSVCModel =
pipeline.stages(1).asInstanceOf[LinearSVCModel]

lazy val coefficients: Array[Double] = svcModel.coefficients.toArray
lazy val rFeatures: Array[String] = if (svcModel.getFitIntercept) {
Array("(Intercept)") ++ features
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@yanboliang yanboliang May 19, 2017 •
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In R we stack intercept with other feature names, you can refer spark.glm, spark.logit, spark.survreg.

} else {
features
}

lazy val intercept: Double = svcModel.intercept
lazy val rCoefficients: Array[Double] = if (svcModel.getFitIntercept) {
Array(svcModel.intercept) ++ svcModel.coefficients.toArray
} else {
svcModel.coefficients.toArray
}

lazy val numClasses: Int = svcModel.numClasses

Expand Down